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Summary: Models and Issues in Data Stream Systems
Brian Babcock Shivnath Babu Mayur Datar Rajeev Motwani Jennifer Widom
Department of Computer Science
Stanford University
Stanford, CA 94305
¡ babcock,shivnath,datar,rajeev,widom¢ @cs.stanford.edu
Abstract
In this overview paper we motivate the need for and research issues arising from a new model of
data processing. In this model, data does not take the form of persistent relations, but rather arrives in
multiple, continuous, rapid, time-varying data streams. In addition to reviewing past work relevant to
data stream systems and current projects in the area, the paper explores topics in stream query languages,
new requirements and challenges in query processing, and algorithmic issues.
1 Introduction
Recently a new class of data-intensive applications has become widely recognized: applications in which
the data is modeled best not as persistent relations but rather as transient data streams. Examples of such
applications include financial applications, network monitoring, security, telecommunications data manage-
ment, web applications, manufacturing, sensor networks, and others. In the data stream model, individual
data items may be relational tuples, e.g., network measurements, call records, web page visits, sensor read-
ings, and so on. However, their continuous arrival in multiple, rapid, time-varying, possibly unpredictable
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